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A Comparative Study of Recent Multi-Component Unmixing Algorithms

EasyChair Preprint no. 4753

5 pagesDate: December 20, 2020

Abstract

In this paper, we consider the problem of blind multicomponent image unmixing. Two mixing models are considered : the linear mixing model (LMM) and its extended version (ELMM) which take the spectral (i.e. endmember) variability into account. We introduce powerful unmixing algorithms utilizing these models of latest state-of-the-art, and compare their performance on endmember recovery and abundance estimation.

Keyphrases: Evaluation Assessment, hyperspectral image, multispectral image, spectral unmixing

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:4753,
  author = {Zhang Mo and Stéphane Pezeril},
  title = {A Comparative Study of Recent Multi-Component Unmixing Algorithms},
  howpublished = {EasyChair Preprint no. 4753},

  year = {EasyChair, 2020}}
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